Systems and methods for characterizing transmission lines using broadband signals in a multi-carrier DSL environment

ABSTRACT

Using DSL modems as data collectors, the modems processes the data to, for example, allow easier interpretation of the line characteristics. In particular, the modems postprocess the data including calibration, filter compensation, determination of the SNR medley from the bits and gains tables and rate conversion. The interpretation process uses the postprocessed data and determines loop characterization, interferer detection, a data reduction estimation and a data rate estimation. The outputs of these determinations least allow for the characterization of the line conditions between the two modems.

RELATED APPLICATION DATA

This application claims the benefit of and priority to U.S. ProvisionalApplication Ser. No. 60/224,308 filed Aug. 10, 2000 entitled“Characterization of Transmission Lines Using Broadband Signals in aMulti-Carrier DSL System” and is related to U.S. patent application Ser.No. 09/755,172, filed Jan. 8, 2001 entitled “Systems and Methods forLoop Length and Bridged Tap Length Determination of a Transmission Line”and U.S. patent application Ser. No. 09/755,173, filed Jan. 8, 2001,entitled “Systems and Methods for Establishing a Diagnostic TransmissionMode and Communicating over the Same,” all of which are incorporatedherein by reference in their entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

In general, the systems and methods of this invention relate to thedetermination of transmission line characteristics. In particular, thisinvention relates to systems and methods for determining thecharacteristics of a transmission line using broadband signals.

2. Description of Related Art

Rapid developments in the computer industry and the, availability ofaffordable hardware created the Internet, i.e., a distributed network,wherein a user having a communications link between themselves and acomputer in a centralized location can access publicly availableinformation. Users of the Internet are connected to the distributednetwork through a link that includes, for example, a telephone line froma customer premises (CPE) to a telephone company central office (CO). Auser requesting a data transfer from an Internet server is faced withthe limited bandwidth of the connection between their location and thecentral office. As more and more information is being created and storedin digital format, the demand for users to access large data files isincreasingly making it crucial to find new and faster ways oftransferring data. One way of achieving faster data transmission is toincrease the bandwidth of the transmission line between the users andthe CO by, for example, replacing the current metallic conductors withfiber or using better quality metallic conductors having increasedbandwidth. However, such an approach is costly and requires asubstantial investment by the telephone companies.

Recent developments in digital signal processing and telecommunicationshave resulted in the digital subscriber line (DSL) technology enabling ahigh speed data link over existing twisted pair telephone lines.Although a couple of different DSL systems have been proposed,multi-carrier systems have quickly gained popularity and are becomingstandardized. Multi-carrier DSL systems operate on the principle offrequency division multiplexing, wherein separate frequency bands areused to transfer data from the CPE to the CO and vice versa. The portionof the bandwidth allocated for transmitting data from the user to the COis called the upstream (US) channel, and the portion of bandwidthallocated for passing data from the CO to the user is called thedownstream (DS) channel. Since in a typical Internet session the amountof data being transferred from the CO to the user is much larger thanthe amount of data transmitted from the user to the CO, the bandwidthallocated for the downstream channel is usually much larger than thebandwidth allocated for the upstream channel. Typical ratios ofdownstream to upstream channel bandwidth are 4:1 or 8:1.

The bandwidth allocated to the upstream and downstream channels ispartitioned into a large number of sub-bands which are sufficientlynarrow so as to allow the distortions introduced by the line to bedescribed as an attenuation and a phase shift. These parameters can bemeasured in a training session prior to establishing the data link bysending and receiving a predefined signal on a sub-band. The amount ofdata that can be sent in a sub-band is limited by the signal to noiseratio (SNR) in that sub-band, which is the signal strength described bythe line attenuation divided by the noise power. Each of the sub-bandsin the multi-carrier DSL system is used to transmit data that isconsistent with the SNR on that sub-band and maximum allowable bit errorrate (BER). A multi-carrier DSL system operating within the principlesdescribed above is able to achieve data rates that are as high as, forexample, ten million bits per second.

SUMMARY OF THE INVENTION

Although the multi-carrier DSL systems are promising because they offera cost-effective way of opening current telephone lines to high-speeddata transmission traffic, there are important problems in theinstallation and maintenance phases of DSL deployment that prevent rapidand widespread deployment. For example, existing telephone lines wereinitially installed for voice-only transmission. This voice-onlytransmission can be successfully transmitted using only a smallbandwidth. Multi-carrier DSL systems require utilizing a bandwidth muchlarger than that required by the voice transmission. At highfrequencies, line conditions that do not affect the voice transmissionbecome important factors limiting the digital data transmission rate.For example, the line attenuation is related to the loop length. Also,the strength of the signal sent from either the CO or the user willdecrease with distance. Additionally, small, open-circuited, twistedpairs, called bridged taps, connected in shunt with working twistedpairs, while not affecting voice transmission, cause periodic dips inthe attenuation function of the line at certain sub-bands and hencedegrade the performance of the DSL service. Additionally, telephonelines are usually bundled as 25 or 50 twisted pairs in a cable. Theclose proximity of the twisted pairs in the cable causes the signalsgenerated by the various DSL services carried by a specific telephoneline to be picked up by one or more of the remaining telephone lines inthe bundle. These signals are perceived as additive noise componentsbecause they are unpredictable and meaningless for all but one of thetelephone line carrying the actual service. The interference enteringthe telephone lines through some coupling path with other telephonelines is referred to as crosstalk.

There may be other sources of noise in a telephone line which are causedby the reception of electromagnetic (EM) waves transmitted by varioussources such as AM radio stations, electrical devices such as hairdryers, dimmer switches, alarm systems, or the like. The mostdetrimental of these electromagnetic sources are generally the AM radiostations. Since no two telephone lines are the same, and theavailability and the quality of a DSL link is directly proportional tothe conditions of the line as described above, it is important to beable to qualify telephone lines for DSL service and maintain thecommunications link once the service is established. To decrease thecosts associated with service qualification and maintenance, it may bedesirable to qualify and maintain telephone lines remotely, withouthaving to send a technician to the customer premises.

Establishing a communications link between a user and one or moreservers connected to the backbone of the central office requires a DSLtransceiver to handle the data transmission in accordance with the basicprinciples outlined above. Each of the transceivers at either side ofthe link, i.e., the CO and the CPE, are called modems. The CO and theCPE modems comprise some analog hardware to perform analog signaltransmission and reception, and a digital section which comprise adigital signal processing (DSP) chip and, for example, an ApplicationsSpecific Integrated Circuit (ASIC) that handles signal processingoperations. Because of the high data rate associated with DSL service,the DSP chip should be able to complete the necessary processing andmanipulation of digital data quickly and efficiently. An exemplaryembodiment of the present invention takes advantage of the vastcomputational capacity of DSL modems and the presence of the DSP chipsat the two sides of the transmission line to characterize thetransmission line. While the DSL modems can operate as a modem in theirusual state, they are also capable of operating in a separate mode wherethey can be used as test and measurement devices.

An exemplary issue faced during the installation and maintenance of DSLservice is the determination of the physical structure and the conditionof the line so that a decision can be made regarding the suitability ofthe loop for DSL service, and which steps can be taken, if any, toimprove the telephone line so that the service providers can offerbetter DSL service. For example, if a bridged tap causing a substantialdata rate reduction is found, the telephone company may send atechnician to remove the bridged tap. In general, the loop length, thedetection of the bridged taps and the estimation of their lengths andlocations, and the detection of interferers on the line is useful forcharacterizing the transmission line.

Additionally, after the installation of the DSL hardware, the link mustbe monitored in order to ensure continued service quality. Thisgenerally requires determining changes in the transmission environmentwhich can again, for example, be accomplished by using the signalprocessing capabilities of the DSL modem.

In accordance with an exemplary embodiment of this invention, the CO andCPE modems are used as test points. The test process comprisescollecting specific data sets during modem training, postprocessing thedata to facilitate the use and interpretation thereof, and extractingresults regarding the line condition. In modem training, the objectiveis to perform measurements and determine the parameters of thetransmission line so as to allow restoration of the original signalstransmitted by the CPE and the CO modems. These signals are generallydistorted by the transmission line through attenuation and phase shift,and further degraded by noise. The CO and CPE modems go through apre-defined and standardized set of states to learn the parameters ofthe entire communications system. They transmit and receive signalsknown to each modem. These signals help in characterizing thetransmission line. For example, in accordance with an exemplaryembodiment of this invention, data collection software and/or hardware,i.e. a module, is added to either or both of the CO and the CPE modems.This data collection module allows some of the data sets already used inthe modem training to be collected with and saved for further analysis.The data collection module also allows additional and new data to beobtained.

Since the CO and the CPE modems operate based on frequency divisionmultiplexing, the data collected at the CPE and CO modems are differentin the sense that the CPE modem transmits in the upstream channel andreceives in the downstream channel, and the CO modem transmits in thedownstream channel and receives in the upstream channel. Therefore, thebandwidth of the data collected at the CPE modem is limited to thebandwidth of the downstream channel and similarly, the bandwidth of thedata collected at the CO modem is limited to the bandwidth of theupstream channel. Therefore, as a result of the modem training, theupstream data can be collected and saved in the CO modem. Likewise, thedownstream data can be collected and saved at the CPE modem. This typeof test process makes use of the standard modem training procedures andtherefor relies on the existence of both the CO and the CPE modem. Thiswill be referred to as a double-ended test.

In a double-ended test, the downstream data collected at the CPE modemcan be transferred to the CO modem to, for example, be further analyzedby service technicians and/or additional hardware and/or software. Thisrequires the ability to establish a special diagnostic link between theCO and the CPE modems for transmitting the diagnostic data, even if thestandard DSL link fails. This can be accomplished, for example, by themethod described in co-pending U.S. application Ser. No. 09/755,173. Inthe case where a diagnostic link cannot be established, only local data,i.e., the upstream data at the CO modem, and the downstream data at theCPE modem, will be available for analysis.

One or more entities, such as a telephone company, may also want toperform a single-ended test from either the CO or the CPE modem to, forexample, pre-qualify customer lines for DSL service. Additionally, forexample, a computer manufacturer who installs DSL modems into itscomputers may want to perform a single-ended test so that a customer candetermine what type of DSL service to order. In these cases, the signalprocessing capabilities of the DSL modem can be utilized in a differentfashion. In a double-ended test, one of the modems acts as a signalgenerator and the other works as a signal receiver. In a single-endedtest, the same DSL modem acts as both the signal generator and thesignal receiver for characterizing the communications link.

In accordance with an exemplary embodiment of the invention, an aspectof the invention relates to the postprocessing and interpretation ofdata collected on a communications link.

An additional aspect of the invention relates to collecting data fromone or more of a CO and a CPE modem.

Additionally, aspects of the invention also relate to manipulating dataat one or more ends of a communication system to ease subsequent use andinterpretation of the data.

Additionally, aspects of the invention also relate to one or more ofcalibrating, filter compensating, estimating of remote SNR tables, anddata rate converting the data obtained from one or more of the CO andCPE modems.

Additional aspects of the invention also relate to outputting easy tointerpret results about the line conditions.

Additional aspects of the invention also relate to outputting easy tointerpret results about the communication link between the CPE and theCO.

These and other features and advantages of this invention are describedin or apparent from the following detailed description of theembodiment.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention will be described in detail, withreference to the following figures wherein:

FIG. 1 illustrates an exemplary line characterization system accordingto this invention;

FIG. 2 illustrates an exemplary method of determining calibrated dataaccording to this invention;

FIG. 3 illustrates an exemplary method of determining filter compensateddata according to this invention;

FIG. 4 illustrates an exemplary method of reducing the effects of timedomain and frequency domain filters according to this invention;

FIG. 5 illustrates an exemplary method of determining a far-end SNRtable according to this invention;

FIG. 6 illustrates an exemplary method of determining the actual datarate according to this invention;

FIG. 7 illustrates an exemplary loop length model according to thisinvention;

FIG. 8 illustrates an exemplary method of determining loop lengths andbridged tap lengths according to this invention;

FIG. 9 illustrates an exemplary operation of the crosstalk detectionprocess according to this invention;

FIG. 10 illustrates an exemplary method of determining disturbanceinformation according to this invention;

FIG. 11 illustrates an exemplary power spectrum of an AM/EMIinterference pattern;

FIG. 12 illustrates the second derivative of the power spectrum of FIG.11 determined in accordance with this invention;

FIG. 13 illustrates an exemplary method of determining the number ofAM/EMI disturbers according to this invention;

FIG. 14 illustrates an exemplary method of determining a ratedegradation estimate according to this invention;

FIG. 15 illustrates an exemplary method of determining an estimated datarate according to this invention; and

FIG. 16 illustrates an overview of the exemplary function of determiningcommunications link characteristics according to this invention.

DETAILED DESCRIPTION OF THE INVENTION

The exemplary embodiments of this invention will be described inrelation to the application of the invention to an ADSL transceiverenvironment. However, it should be appreciated that in general thesystems and methods of this invention will work equally well for anymulticarrier communication system including, but not limited to DSL,VDSL, SDSL, HDSL, HDSL2, or any other discrete multi-tone or discretewavelet multi-tone DSL system.

FIG. 1 illustrates an exemplary line characterization system 100. Theline characterization system 100 comprises one or more CO modems 110,one or more CPE modems 130 and a postprocessing and interpretationmodule 150. Additionally, the CO modem 110 comprises a data collectionmodule 120. Likewise, the CPE modem 130 comprises a data collectionmodule 140. The processing and interpretation module 150 comprises aninterpretation parameter storage 160 and is connected to one or moreoutput devices 170.

For ease of illustration, the standard components associated with the COmodem 110 and a CPE modem 130 have been omitted although are readilyidentifiable by one of ordinary skill in the art. Furthermore, thepostprocessing interpretation module 150 has been simplified but caninclude, for example, a controller, an I/O interface, a memory, and/ormay be implemented on a digital signal processor, an ASIC, or anyhardware and/or software combination that is capable of performing thefunctions described herein. The postprocessing interpretation module 150is also connected to one or more output devices 170 such as a printer,monitor, line characterization display system, PDA, graphical userinterface, network monitoring system, DSL analysis system, or the like.

While the exemplary embodiment illustrated in the FIG. 1 shows the linecharacterization system 100 and various components separated, it is tobe appreciated that the various components of the line characterizationsystem can be combined or located at distance portions of a distributednetwork, such as a local area of network, a wide area network, anintranet and/or the Internet, or within a dedicated linecharacterization system. Thus, it should be appreciated, that thecomponents of the line characterization system 100 can be combined intoone device or collocated on a particular node of a distributed networkor combined into one or more of a CO or CPE modem. Thus, it will beappreciated from the following description, and for reasons ofcomputational efficiency, that the components of the linecharacterization system 100 can be arranged any location, such as in ageneral purpose computer or within a distributed network or dedicatedline characterization system without affecting the operation of thesystem.

As discussed above, the data collection modules 120 and 140, which canbe a combination of hardware and/or software, at least allow for thedata sets used in modem training to be collected and saved. Furthermore,the data collection modules 120 and 140 allow for the collection of newdata or data sets that can be obtained either during training or inshowtime. Thus, one or more data sets are collected from either the datacollection module 120 and/or the data collection module 140 andforwarded to the postprocessing interpretation module 150 for analysis.

For example, as discussed above, in the event it is difficult toestablish a communication link between a modem and the postprocessingand interpretation module 150, a diagnostic link can be established suchas that described in co-pending U.S. application Ser. No. 09/755,173.However, in general, any protocol or method that is capable offorwarding the data from one or more of the CEO and CPE modems can workequally well with the systems and methods of this invention.

After data collection, the postprocessing and interpretation module 150processes the data to, for example, allow easier interpretation of theline characteristics. In particular, the postprocessing process includescalibration, filter compensation, determination of the SNR medley fromthe bits and gains tables and rate conversion. The interpretationprocess includes, with the cooperation of the interpretation parameterstorage 160 that stores one or more parameters, loop characterization,interferer detection, a data reduction estimation and a data rateestimation.

In general, the postprocessing involves various tasks such as convertingthe raw data from one format to another, scaling the data andcompensating for the analog and digital filters in the transmissionpath.

In general, during the interpretation process, the exemplary loop lengthestimation procedure estimates the loop length and attempts to determinethe presence of one or more bridged taps on the transmission line. If abridged tap is detected, the length of the bridged tap is alsoestimated. The estimation is performed by comparing a model of thetransfer function of the line, which is parameterized in terms of theloop length and the bridged tap lengths and locations, to the actualmeasured transfer function of the line. Three different algorithms areused to estimate the physical structure of the loop depending on whichdata set is being used, i.e., upstream, downstream, or single-ended timedomain reflectometry.

The interferer detection process identifies crosstalk andelectromagnetic disturbers on the line by analyzing the measured powerspectrum of the noise. The data rate reduction estimation estimates thedata rate reduction caused by the presence of the disturbers on thetransmission line. Similarly, the data rate estimation estimates themaximum data rate the transmission line can support through the use of asingle-ended test. The test combines the results of the single-endedtime domain reflectometry test and the measurement of the power spectrumof the noise on the line to estimate a rough SNR profile for both theupstream and the downstream channels as well as estimates the data ratebased on these SNR tables.

FIG. 16 illustrates an overview of the method for performingcommunications link characterization. Specifically, control begins instep S10 and continues to step S20. In step S20, raw data is obtainedfrom one or more of a CO modem and a CPE modem. Next, in step S30,postprocessing is performed on a portion of the raw data. Then, in stepS40, interpretation is performed on one or more of a portion of the rawdata and a portion of the postprocessed data. Control then continues tostep S50.

In step S50, the communications link, i.e., line, condition informationis output in, for example, a visually displayable format. Control thencontinues to step S60 where the control sequence ends.

FIG. 2 outlines an exemplary method for performing a calibration thatmodifies the collected data so that the data appears as if it had beenmeasured, for example, with standard test equipment. In particular, thecalibration routine takes the received data, which can come in the formof a raw data, the programmable gain amplifier (PGA) settings used tocollect the data, and the gain scaling, if any, and outputs thecalibrated data. However, this calibration function and the resultingcalibrated data may vary depending on the actual implementation and theraw data being analyzed.

In particular, control begins in step S100 continues to step S110. Instep S110, a raw data array is received. Next, in step S120, the numberof elements in the raw data array is determined. Then, in step S130, thePGA settings that were present during the data collection process aredetermined. Control then continues to step S140.

In step S140, the scaling information that was applied to the receivedraw data is determined. Next, in step S150, and for a predeterminednumber of iterations, step S160 is performed. In particular, in stepS160, an output array containing the calibrated data is determined.Control then continues to step S170.

In step 170, the calibrated data array is output. Controlling continuesto step S180 where the control sequence ends.

The filter compensation routine removes the effects of the analogfront-end (AFE) filters from the received data. In particular, thefilter compensation routine modifies the calibrated data based on thedevice specific frequency domain response of the AFE filters, andoutputs the filter compensated data.

FIG. 3 illustrates an exemplary method of performing the filtercompensation. In particular, control begins in step S200 and continuesto step S210. In step S210, the calibrated data array is received. Next,in step S220, the device specific frequency domain filter function, indB, is received. Then, in step S230, the number of elements in thecalibrated data array is determined. Control then continues in stepS240.

In step S240, for a predetermined number of iterations, step S250 isperformed. In particular, in step S250, the filter compensated data,which is an array containing the filter compensated and calibrated datais determined. Next, in step S260, the filter compensated data array isoutput. Control then continues in step S270 where the control sequenceends.

In, for example, service monitoring, the CPE and the CO modems collectthe reverb signal received in a sync frame. Since time domainequalization and frequency domain equalization are normally in operationduring showtime, the received reverb signal is affected by the timedomain equalization and frequency domain equalization filters. Throughthe use of frequency domain deconvolution, it is possible for thepostprocessing and interpretation module 150 to remove the effects ofthe time domain equalization and the frequency domain equalization.

In particular, FIG. 4 outlines an exemplary method of reducing theeffects of the time domain and frequency domain equalization filters.Control begins in step S300 and continues in step S310. In step S310,the calibrated data array in dB is received. This calibrated data can beeither before or after the correcting for the time and frequency domainequalization. Next, in step S320, an array with the time domainequalization filter coefficients are determined. For example, the timedomain equalizer coefficients can be stored in the CO/CPE modem aftertraining so the system need only access the stored coefficients. Then,in step S330, an array with the frequency domain equalizer filtercoefficients, in dB, is determined. For example, the frequency domainequalizer coefficients can be stored in the CO/CPE modem after trainingso the system need only access the stored coefficients. Control thencontinues to step S340.

In step S340, the number of elements in the calibrated data array isdetermined. Next, in step S350, for a predetermined number ofiterations, the Fast Fourier Transformed of the time domain equalizationcoefficients is determined. Control then continues to step S370.

In step S370, and for a predetermined number of iterations, adeconvolution in the log frequency domain is performed in step S380 todetermine the compensated data value. In step S390, the compensateddata, which reduces or removes the effects of the time domainequalization and the frequency domain equalization, is output. Controlthen continues to step S395 where the control sequence ends.

In two-ended provisioning, if a CO or CPE modem is not capable ofestablishing a diagnostic link, only the local upstream or downstreamdata is available. However, a representation of the SNR table at the farend modem can be obtained through a standard link. According to theG.dmt and G.Lite specifications, each of which are incorporated hereinby reference of their entirety, each modem sends a bits and gains tableto the corresponding upstream or downstream modem. This table indicatesthe number of bits assigned to each tone and the corresponding finegain. Since the bit allocation table is directly related to the SNR, thepostprocessing and interpretation module 150 is able to perform areverse transformation from the bits and gains table to the SNR table.

In particular, FIG. 5 outlines an exemplary method of determining theSNR medley from the bits and gains table. In particular, control beginsin step S400 and continues in step 410. In step S410, the far-end bitloading table is received. In step S420, the far-end fine gains table isreceived. Then, in step S430, the number of elements in the bits andgains arrays is determined. Control then continues to step S440.

In step S440, the required SNR array is determined. For example, therequired SNR array can be a predetermined pre-set array for the specificDSL application. This array can be obtained from, for example, theG.lite, G.992.1, and G.992.2 specifications, each of which areincorporated herein by reference in their entirety. The SNR array canalso be stored in the CO/CPE modem software to be used in the bitloadingphase of the modem initialization. Next, in step S450, the margin isdetermined. The margin is a parameter that determines by how much theSNR will be reduced in determining the bit table. For example, a marginof 6 dB means that when assigning the bit table, the SNR at each bitwill be reduced by 6 dB. Therefore, the margin provides the system witha SNR cushion against sudden noise bursts. Next, in step S460, for apredetermined number of iterations, the SNR table is estimated in stepS470. Control then continues to step S480.

In step S480, the estimated far-end SNR table is output. Control thencontinues in step S490 where the control sequence ends.

In addition to the above, the postprocessing interpretation module 150is also capable of converting the data rate of the received data array.In particular, and in accordance with an exemplary embodiment, the dataarray is converted, based on units of 32 Kbps, to the actual data ratein Kbps.

FIGS. 6 outlines an exemplary method of converting the data rate. Inparticular, control begins in step S500 and continues in step S510. Instep S510, the raw data rate is determined. Next, in step S520, the rawdata rate is converted to the actual data rate in Kbps. Then, in stepS530, the actual data rate is output. Control then continues the stepS540 where the control sequence ends.

The interpretation portion of the postprocessing and interpretationmodule 150 extracts comprehensible results from the postprocessed data.In particular, as discussed above, during interpretation, thepostprocessing and interpretation module 150 is at least capable ofperforming loop characterization, crosstalk and disturber estimation, AMradio and electromagnetic interference detection, rate degradationestimates and data rate estimates.

In particular, an exemplary method of loop characterization that workswith the systems and methods of this invention employs a model basedapproach to estimate the length of the loop and the lengths of up to twobridged taps. Specifically, as illustrated in co-pending applicationSer. No. 09/755,172, a comparison is made between the measured channelimpulse response and the channel impulse response of a loop modelconsisting of a single-gauge wire and containing up to two bridged taps.The loop length and the bridged tap lengths are the parameters of thetheoretical channel impulse response. The algorithm changes theparameters of the theoretical model and evaluates the difference betweenthe measured channel impulse response and the theoretical channelimpulse response. The loop length and/or bridged tap lengths thatminimize the error functions are then declared as the estimated values.

While the above described method takes advantage of a double-endeddiagnostic mode whereby the CO and CP modems are available, if the CPEmodem is not yet installed or is not operational, the postprocessing andinterpretation module can perform a time domain reflectometry (TDR)technique that can be used to estimate the physical structure of theline.

Specifically, the data required by the time domain algorithm is obtainedby sending a pre-defined signal over the channel and evaluating the echowaveform. The echo obtained in this way is analyzed to detect theimpedance discontinuity caused by any bridged taps, an open-end of theloop, load coils, or the like. An echo cancellor (not shown) can berunning during the time domain reflectometry measurements in order tocancel the near-end echo caused by the analog front-end (AFE) circuitryof the line card.

If x_(k)(n), n=1, . . . , n, where N is the number of signal sampleswithin a frame, is the sampled version of the received signal at thek^(th) frame at the output of the echo cancellor, the TDR waveformbecomes: ${{TDR}(n)} = {\frac{1}{K}{\sum\limits_{k = 1}^{K}{x_{k}(n)}}}$Note that the TDR waveform is obtained by time-domain averaging.Therefore, the FFT in the receive path will be turned off during theaveraging process.

In theory, any impedance discontinuity in the loop causes a reflectionwhich is observed as a pulse whose location and height can be used toestimate the distance of the impedance discontinuity as well as thetype, i.e., whether the impedance discontinuity is caused by a bridgedtap or open-end of the loop. If multiple impedance discontinuities arepresent in the loop, analyzing the time domain waveform of the echosignal becomes very complicated. For this reason, a model based approachcan be used for the TDR estimations.

The exemplary method generally compares the observed echo with that of amodel where the channel is assumed to consist of three sectionsseparated by two bridged taps as shown in FIG. 7. An objective of theTDR analysis is to estimate d_(i),=1, 2, 3 and b_(j),j=1, 2 whichprovide information about the location and the lengths of the bridgedtaps as well as the length of the entire loop.

In the measurement phase, all the phones in the customer premises shouldbe on-hook. This is necessary since the loop model assumes that the endof the loop is null terminated, i.e., open. This requires detection ofon/off-hook conditions prior to the TDR measurements.

Next the TDR measurement is conducted by averaging the echo signal overK frames and recording the result. This procedure results in a timedomain echo waveform which will be compared with the echo response of aknown loop.

The theoretical model for the echo channel transfer function in theupstream case can be described in two steps. The first step consists ofwriting the equations for the current and the voltage at the source (COTransmit), I_(s), V_(s), in terms of the current and the voltage at theload (CO Receive), I_(L), V_(L), through the application of ABCDmatrices. Thus, the echo response of a loop given d_(i) and b_(j) isgiven by: $\begin{bmatrix}V_{S} \\I_{S}\end{bmatrix} = {F^{s} \times A^{1} \times B^{1} \times A^{2} \times B^{2} \times A^{3} \times F^{L} \times \begin{bmatrix}V_{L} \\0\end{bmatrix}}$where A^(i), B^(j), F^(S) and F^(L) are 2×2 matrices whose elements arein fact arrays of N/2 elements where N is the number of samples in theTDR waveform buffer or frame as before. Here, A^(i) is a matrixrepresenting the frequency domain response of the i^(th) section of theloop, B^(j) is the matrix representing the response of j^(th) bridgedtap, and F^(S) and F^(L) are the matrices representing the AFE circuitryfor TX (source) and RX (load) paths. From the above transfer function,the echo path can be derived and is given by:$H_{echo} = \frac{V_{L}}{V_{S}}$

Entries of the above matrices are as follows:A ^(i) ₁₁ =A ^(i) ₂₂=cos h(γd _(i))A ^(i) ₁₂ =Z ₀ sin h(γd _(i)), A ^(i) ₂₁ =A ₁₂ Z ⁻² ₀

Entries of matrix B^(j):B^(j) ₁₁=B^(j) ₂₂=1B^(j) ₁₂=0, B^(j) ₂₁=Z⁻¹ _(j)

Where Z⁻¹ _(j) is a quantity related to the impedance of the j^(th)bridged tap and finally:F^(S) ₁₁=F^(S) ₂₂=1, F^(S) ₁₂=0, F^(S) ₂₁=Z_(S)F^(L) ₁₁=F^(L) ₂₂=1, F^(L) ₁₂=0, F^(L) ₂₁=Z⁻¹ _(L)From these equations the required memory size can be determined. As anexample, each of the entries of the matrices can be arrays of 128complex elements. Since it can be complex to determine the cos h(γd) andthe sin h(γd) values, these quantities can be predetermined in regularintervals, such as, 500 ft intervals, from 5 kft to 15 kft. Theseexemplary predetermined intervals would require 42×256 locations forstoring the cos h(.) and the sin h(.) values, and 256 locations forstoring Z₀ ⁻². Assuming the bridge tap lengths can be distinguished in250 ft increments, 6×256 locations would need to be allocated for Z_(j)⁻¹. Additionally, 2×256 locations for storing the Z_(S) and the Z_(L) ⁻¹values would be needed. This totals 52×256 locations. Also, 8×256locations would be needed for storing the intermediate results of themultiplications.

Given the theoretical echo transfer function of the system, the looplength and bridged tap lengths and locations are estimated by minimizingthe following with respect to d₁, d₂, d₃ and b₁, b₂:$\min\limits_{d_{i},b_{j}}{{{TDR} - {H_{echo}\left( {d_{i},b_{j}} \right)}}}^{2}$Thus, a search must be performed over the d_(i) and b_(j) parameters.From the location of the first reflected pulse d₁ and b₁, if thereflection was caused by a bridge tap, the location can be estimated.Therefore, d₁ and b₁ can be eliminated from the search. The first threematrices in the expression for echo response, F^(S)×A¹×B¹ can be lumpedtogether and need not be considered. For each set of search parameters,d₂, b₂, d₃, the echo response is constructed. Then, the differencebetween the actual and theoretical echo responses is determined. Thisprocedure is repeated as many times as needed by the search algorithm.Since the search algorithm generally needs a variable number ofiterations to arrive at the optimal d₂, d₃, b₂ values, this number isdifficult to predict.

FIG. 8 illustrates an exemplary method of characterizing the loop usingtime domain reflectometry. Specifically, control begins in step S550 andcontinues to step S555. In step S555, a determination is made whetherthe phones are on-hook. If the phones are on-hook, control jumps to stepS565. Otherwise control continues to step S560. In step S560, the phonesare placed on-hook. Control then continues to step S565.

In step S565, a predefined signal is sent over the channel and the echowaveform analyzed. Next, in step S570, the TDR waveform is determined byaveraging the echo signal over K frames. Then, in step S575, the looplength and the bridged tap lengths of the model are varied. Control thencontinues to step S580.

In step S580, the difference between the measured channel impulseresponse and the theoretical channel impulse response is monitored.Next, in step S585, the estimated values are declared based on the looplength/bridged tap length that minimizes the error function. Controlthen continues to step S590 where the control sequence ends.

Aside from estimating the elements such as the loop length and thebridged tap lengths that form the physical structure of a loop, theinterpretation procedure is also capable of identifying variouscrosstalk and disturbance sources on the channel. Twisted cable pairsare typically bundled as 25 or 50 pair units. Different DSL services,such as HDSL, T1 or ISDN, carried by one or more of the twisted pairsare usually picked up by the remaining twisted pairs in the bundle andobserved as noise sources. The interference entering a twisted pairthrough some coupling path with the other twisted pairs are calledcrosstalk.

There are other sources of disturbance on the line that are caused byelectromagnetic coupling. A good example is AM radio stations. Faultyin-home wiring usually results in the observation of AM signals in theDSL frequency band. An objective of the crosstalk/disturber estimationalgorithms is to identify the crosstalk sources and provide quantitativeinformation about the sources such as power level and frequency of thedisturber. The identification of a crosstalk/disturber on the line isfollowed by a rate degradation estimation which is a prediction of thedata rate loss caused by the presence of the identified disturber.

From an algorithmic point of view, there are two different algorithmsthat identify the crosstalk and ElectroMagnetic Interference (EMI).After a discussion of the data collection process, these two algorithmswill be described in detail.

The interferer detection procedure uses the power spectrum of the idlechannel noise (ICN) for the estimation of the crosstalk/disturbers onthe line. During the ICN measurement, the channel is monitored to ensurethat there are no meaningful signals, such as an activation requesttone, on the line. If a signal present on the receiver is denoted asx(n), where n=1, . . , N is the sample index within a frame, and N isthe number of samples contained in a frame, the power spectrum,S_(xx)(f), is estimated according to:${S_{xx}(f)} = {\frac{1}{K}{\sum\limits_{k = 1}^{K}{{{FFT}_{N}\left( {x_{k}(n)} \right)}}^{2}}}$where x_(k)(n) is the sampled signal collected during the k^(th) frameand K is the number of frames over which the above averaging isperformed. In other words, the N-point signal sequence x_(k)(n) issampled at the k^(th) frame, the N-point FFT taken and the average ofthe square of the magnitudes of the FFT coefficients for K consecutiveframes determined. This procedure provides the periodogram estimate ofthe power spectrum. As was the case with the reverb signal measurements,the power spectrum is available only at a discrete set of frequencies,ƒi=iΔƒ, i=i_(ƒ), . . . , i_(l), where i_(ƒ) and i_(l) denote the firstand the last tones where the power spectrum is sampled. The accumulationprocess continues until the desired precision in noise measurementprocess is obtained. For example, K=512 or K=1024 accumulations shouldprovide excellent results.

The crosstalk type and power are estimated by comparing the measurednoise power spectrum to known crosstalk spectral masks such as DSL Next,HDSL Next, T1 Next, or the like. The algorithmic steps are to minimizewith respect to i, where i denotes the ith known disturber, g, which isthe power of the disturber, and σ, which represents the power of thewhite noise, the square of the difference between the observed and theknown interferer power spectral masks. The disturber which minimizes themean square error (MSE) in accordance with:${{MSE}_{i}\left( {g,\sigma} \right)} = {\sum\limits_{n = 6}^{256}{{{{PSD}_{ICN}(n)} - \left( {{g^{2}{{PSD}_{i}(n)}} + \sigma^{2}} \right)}}^{2}}$is determined.

In the above algorithm, i, g and σ, which are associated with thedisturber type, power and white noise level, respectively, are variedand the set of variables which minimizes the MSE over all candidatecrosstalk types chosen. For example i=1 may denote a DSL Next disturberand g may denote its power. As an example, the memory requirements forthis algorithm can be 256 locations to store the ICN power spectra and256 locations to store the power spectra of each known disturber. Ifthere are P different types of known disturbers the storage requirementis P×256. However, it should be noted that the storage requirements canbe reduced by determining the PSD of the given crosstalk on the flyrather than using the 256 locations to store the entire spectrum.Therefore, data memory can be traded off with program memory andapproximately 350 additional locations for storing intermediatevariables can be used during the exemplary execution of the MSE searchalgorithm.

As for the search algorithm which will be used to determine theparameters i, g and σ which minimize the MSE, it is straightforward todetect the background white noise level so this noise level can bedropped from the search algorithm. What remains is minimizing the MSEwith respect to g for each i which can be accomplished by picking Qpossible values for g and finding, over these Q predetermined values,the one minimizing the MSE. Typical exemplary values for P and Q areP=5, i.e., five known disturber PSD's, and Q=50.

An example of the operation of the crosstalk detection algorithm isillustrated in FIG. 9. The solid line is the measured PSD of the ICNversus the PSD of the crosstalk that best matches the observed data. Theactual disturbance on the line was a DSL Next disturber with −35 dBmpower and the crosstalk detection algorithm found exactly the sameanswer.

FIG. 10 illustrates an exemplary method of determining an estimation ofthe crosstalk. In particular, control begins in step S600 and continuesto step S610. In step S610, an array containing the channel noise isreceived. Next, in step S620, minimization with respect to the i^(th)disturber is accomplished by varying the power of the disturber and thewhite noise. Then, in step S630, the disturber that minimizes the meansquare error in accordance with MSE_(i)(g, σ) is determined. Controlthen continues to step S640.

In step S640, the disturbance information is output. Control thencontinues to step S650 where the control sequence ends.

An ADSL receiver is also susceptible to AM/EMI interference because aportion of the ADSL receive band coincides with the AM and the amateurbroadcast frequencies. According to FCC specifications, the AM radiobroadcast frequencies start at 540 kHz and extend up to 1.8 MHz. Beyondthis frequency band, it is possible to find EMI ingress caused by theamateur radio broadcast in the bands from 1.9 MHz to approximately 3.3MHz. Therefore, home wiring which connects the ADSL modem to thetelephone line can acts as an antenna that detects one or more AM and/orEMI sources.

FIG. 11 illustrates an exemplary power spectrum of a typical AM/EMIinterference pattern with multiple AM interferers. The AM broadcast wasaccomplished by modulating a baseband signal, such as a voice or musicsignal, by amplitude modulation. Denoting the baseband signal by f(t),with t being time, the modulation signal is given by:e _(m)(t)=ƒ(t) cos (ω_(c) t)+A cos (ω_(c) t)where A is a constant and ω=2πf_(c) is the radian carrier frequency.From the above equation, the spectrum of e_(m)(t) consists of thebaseband signal shifted in frequency by ±ω_(c) plus two additionalpulses at ±ω_(c). Therefore,${{FFT}\left( {e_{m}(t)} \right)} = {{\frac{1}{2}\left\lbrack {{F\left( {\omega - \omega_{c}} \right)} + {F\left( {\omega + \omega_{c}} \right)}} \right\rbrack} + {\pi\quad{A\left\lbrack {{\delta\left( {\omega - \omega_{c}} \right)} + {\delta\left( {\omega + \omega_{c}} \right)}} \right\rbrack}}}$

The AM/EMI interference detection is complicated by the fact that theobserved spectrum is dependent on the unknown spectrum of thetime-varying baseband signal f(t) as illustrated above. Thus, the AM/EMIdetection algorithm should use only the carrier frequency of themodulating wave as a signature. The AM/EMI interference frequency andpower can be estimated by modeling the power spectrum of the AM/EMI as aconstant background noise plus a number of spikes, parameterized by thefrequency and height, representing the AM/EMI carrier frequencies. Next,the model is compared with the observed spectrum by varying thefrequency and the height of each individual spike. The frequency/heightconfiguration of the model best matching the original power spectrum interms of mean square error is declared as the estimation.

However, since each spike is parameterized by two parameters, i.e.,frequency and height, each additional AM/EMI disturber adds two moreparameters to the optimization. If, for example, there are 10 AM/EMIdisturbers, optimization would need to be performed over 20 parameters.This, in general, presents a very complicated optimization problem whichmay be difficult to solve in practice. However, analyzing the spectrumof the AM/EMI interferers, it is seen that the first derivative of thespectrum at carrier frequencies is not continuous. That is, at thecarrier frequency, the slope of the spectrum jumps abruptly from apositive large number to a negative large number. Thus, the secondderivative of the spectrum contains large negative pulses and these canbe detected by establishing a negative threshold and determining theimpulses whose heights are below the set threshold.

FIGS. 11 and 12 illustrate the operation of the AM/EMI detection method.Specifically, FIG. 11 shows the power spectrum of the ADSL receiver bandwhich contains a number of AM/EMI disturbers. FIG. 12 shows the seconddifference, which corresponds to the second derivative in continuoustime of the power spectrum in FIG. 11. Large negative spikes at thepoints where the AM/EMI carrier frequencies are located can be observed.The carrier frequencies are detected by locating the points in FIG. 12where the second difference exceeds a predetermined threshold, asillustrated by the dashed line. The power of each AM/EMI disturber isthen estimated directly from the original power spectrum.

FIG. 13 illustrates an exemplary method of determining AM/EMIdisturbers. In particular, control begins in step S700 and continues tostep S710. In step S710, an array containing the channel noise isreceived. Next, in step S720, for a predetermined number of iterations,the second difference of the array containing the idle channel noise isdetermined in step S730. Then, in step S740, for a predetermined numberof iterations, the carrier frequencies that exceed a predeterminedthreshold are detected. Control then continues to step S760.

In step S760, an array containing the tone numbers corresponding to thedetected AM/EMI disturbers is output. Next, in step S770, an arraycontaining the power level of the AM/EMI disturbers is output. Then, instep S780, the number of AM/EMI disturbers are output. Control thencontinues to step S790 where the control sequence ends.

Another function of the postprocessing and interpretation module 150 isto estimate the rate reduction caused by the presence of crosstalkand/or disturbers on the line. If the crosstalk and/or disturberdetection method determines that there are noise sources other than thebackground white noise of the line, the method updates the available SNRtables, which can be obtained through either single-ended ordouble-ended diagnostics, or by reversing the SNR reduction caused bythe disturbers. The methodology then runs a bit loading routine on theupdated SNR table with a given margin, framing and coding information todetermine the rates for a disturber free line. The difference betweenthe actual and the estimated data rates gives the rate reduction causedby the noise sources. The SNR is determined in accordance with:${{SNR}\left( f_{i} \right)} = {10\quad\log_{10}\frac{{{H\left( f_{i} \right)}}^{2}}{S_{xx}\left( f_{i} \right)}}$where H(f_(i)) is the channel impulse response evaluated at the i^(th)tone and S_(xx)(f_(i)) is the power spectral density (PSD) of the noiseon the line evaluated at the i^(th) tone. If there are no noise sourcesof the line, except for the background white noise, the SNR equationcould be simplified to:${{SNR}_{{No} - {Disturber}}\left( f_{i} \right)} = {10\quad\log_{10}\frac{{{H({fi})}}^{2}}{\sigma^{2}}}$where σ is the standard deviation of the white noise. From the aboveequations, once a disturber is detected, the SNR_(No-Disturber) can bedetermined given S_(xx)(f_(i)), the actual PSD of the noise (ICN) and σ.Next, the bitloading routine is run on the SNR_(No-Disturber) and therate difference corresponding to the SNR and the SNR_(No-Disturber)determined.

The method can use the existing bit loading routine for determining theestimated data rate for a disturber-free line. Therefore, the memoryrequired to implement the method can be reduced.

FIG. 14 illustrates an exemplary method for generating the ratedegradation estimate. Specifically, control begins in step S800 andcontinues to step S810. In step S810, the array containing the idlechannel noise is received. Next, in step S820, an array containing theICN with no crosstalk nor AM/EMI disturbers is determined. Then, in stepS830, the SNR medley is deteremined. Control then continues to stepS840.

In step S840, the margin is determined. Next, in step S850, informationabout the framing mode that was used in training collected. Then, instep S860, the coding gain is determined. Control then continues to stepS870.

In step S870, the number of elements in the SNR table is determined.Next, in step S880, the data rate is determined based on the SNR table.Then, in step S890, the SNR reduction caused by the disturber isreduced/eliminated. Control then continues to step S895.

In step S895, the estimated maximum data rate is determined. Next, instep S879, the rate degredation estimate is output. Control thencontinues to step S899 where the control sequence ends.

The TDR data is used to estimate the loop length and bridged tap lengthsas discussed above. Additionally, the information extracted from the TDRinterpretaion method can be used to estimate the frequency domainchannel impulse reasponse H(f_(i)). Furthermore, the PSD of the noiseS_(xx)(f_(i)) is known from the ICN measurements. Thus, the SNR can beestimated from these two quantities in accordance with:${{{SNR}\left( f_{i} \right)} = {10\quad\log_{10}\frac{{{H\left( f_{i} \right)}}^{2}}{S_{xx}\left( f_{i} \right)}}},{i = i_{s}},\ldots\quad,i_{l}$where i_(s) and i₁ are the first and last tones over which the S(f_(i))is evaluated. The data rate is determined by running the bitloadingmethod on the estimated SNR with a given margin, framin and codinginformation. Since the rate estimation algorithm can use existingbitloading routines, again the memory requirements can be reduced.

FIG. 15 illustrates an exemplary method of estimating the data rate.Specifically, control begins in step S900 and continues to step S910. Instep S910, an estimate of the channel attenuation is determined. Next,in step S920, the idle channel noise is determined. Then, in step S930,the margin is determined. Control then continues to step S940.

In step S940, the framing mode information is obtained. Next, in stepS950, the coding gain is determined. Then, in step S960, the SNR tableis obtained. Control then continues to step S970.

In step S970, bit loading is performed on the SNR and an estimated datarate determined. Next, in step S980, the estimated data rate is output.Control then continues to step S990 where the control sequence ends.

As illustrated in FIG. 1, the line characterization system 100 can beimplemented either on a single program general purpose computer, or aseparate program general purpose computer. However, the linecharacterization system 100 can also be implemented on a special purposecomputer, a programmed microprocessor or microcontroller and peripheralintegrated circuit element, an ASIC or other integrated circuit, adigital signal processor, a hard-wired electronic or logic circuit suchas a discrete element circuit, a programmable logic device such as aPLD, PLA, FPGA, PAL, a modem, or the like. In general, any devicecapable of implementing a finite state machine that is in turn capableof implementing the flowcharts illustrated in FIGS. 2-6, 8, 10 and 13-15can be used to implement the line characterization system according tothis invention.

Furthermore, the disclosed method may be readily implemented in softwareusing object or object-oriented software development environments thatprovide portable source code that can be used on a variety of computeror workstation hardware platforms. Alternatively, the disclosed linecharacterization system may be implemented partially or fully inhardware using standard logic circuits or VLSI design. Whether softwareor hardware is used to implement the systems in accordance with thisinvention is dependent on the speed and/or efficiency requirements ofthe system, the particular function, and the particular software and/orhardware systems or microprocessor or microcomputer systems beingutilized. The line characterization system and methods illustratedherein, however, can be readily implemented in hardware and/or softwareusing any known or later-developed systems or structures, devices and/orsoftware by those of ordinary skill in the applicable art from thefunctional description provided herein and a general basic knowledge ofthe computer and communications arts.

Moreover, the disclosed methods may be readily implemented as softwareexecuted on a programmed general purpose computer, a special purposecomputer, a microprocessor, or the like. In these instances, the methodsand systems of this invention can be implemented as a program embeddedon a personal computer such as a Java® or CGI script, as a resourceresiding on a server or graphics workstation, as a routine embedded in adedicated line characterization system, a modem, a dedicated linecharacterization system, or the like. The line characterization systemcan also be implemented by physically incorporating the system andmethod into a software and/or hardware system, such as the hardware andsoftware systems of a line characterization system or modem, such as aDSL modem.

It is, therefore, apparent that there has been provided, in accordancewith the present invention, systems and methods for characterizing lineconditions. While this invention has been described in conjunction witha number of exemplary embodiments, it is evident that many alternatives,modifications and variations would be or are apparent to those ofordinary skill in the applicable arts. Accordingly, the invention isintended to embrace all such alternatives, modifications, equivalentsand variations that are within the spirit and scope of this invention.

1. A multicarrier communication line characterization system comprising:a data postprocessing module; and a data interpretation module, whereinraw data received from one or more modems via a data collection moduleis used to determine the characteristics of a communications link. 2.The system of claim 1, wherein the data processing module performs atleast one of a calibration, a filter compensation, a determination ofthe SNR Medley from a bits and gains table and a data rate conversion.3. The system of claim 1, wherein the data interpretation moduleperforms at least one a loop characterization, a interferer detection, adata reduction estimation and a data rate estimation.
 4. The system ofclaim 1, wherein the communications link is a portion of at least one ofa digital subscriber line communications system, a discrete multi-tonecommunications system or discrete wavelet multi-tone communicationssystem, and the multicarrier communications line characterization systemoutputs visually displayable data about the communications link based ondata obtained from one or more of a CO or CPE modem.
 5. A multicarriercommunications line characterization system comprising: a calibrateddata determination module, wherein a calibrated data is determined basedon a data array, a number of elements in the data array, a programmablegain amplifier setting and a scaling factor.
 6. The system of claim 5,wherein the calibrated data is determined in accordance with:CalibratedData[i]=10*Log₁₀(RawData[i]*2^(GScale))−PGA whereinCalibratedData[i] is a calibrated data array, RawData[i] is datareceived from a modem, GScale is a gain scaling and PGA is aprogrammable gain amplifier setting that was used to collect the dataarray.
 7. A multicarrier communications line characterization systemcomprising: a filter compensated data array determination module thatdetermines a filter compensated data array based on a calibrated dataarray, a frequency domain filter function, and a number of elements inthe calibrated data array.
 8. The system of claim 7, wherein thefrequency domain filter function is based on a device specific frequencydomain response of one or more analog front end filters.
 9. Amulticarrier communications line characterization system comprising: afilter compensated data array determination module that reduces theeffects of one or more of a time domain and a frequency domainequalization filter.
 10. The system of claim 9, wherein the filtercompensated data array is based on a calibrated data array, one or moretime domain equalizer coefficients, one or more frequency domain filtercoefficients, and a number of elements in the calibrated data array. 11.A multicarrier communications line characterization system comprising: afar-end signal to noise ratio table estimator that estimates a far-endsignal to noise ratio table based on a far-end bit loading table, afar-end fine gains table, a number of bits in the far-end bit loadingtable and the far-end fine gains table, a required signal to noise ratioand a margin.
 12. The system of claim 11, wherein the margin is based onthe amount the signal to noise ratio will be reduced in determining thefar-end bit loading table.
 13. A multicarrier communications linecharacterization system comprising: a loop length and bridged tapestimation module that determines an estimate of the loop lengths andpresence of one or more bridged taps based on an echo waveform, a timedomain reflectivity waveform and a comparison to a model of acommunications channel response.
 14. The system of claim 13, wherein theestimate is determined based on a loop length and bridged tap lengththat minimizes an error function.
 15. A multicarrier communications linecharacterization system comprising: a distrubance estimation module thatdetermines a disturbance on a communications link based on an idlechannel noise quantity and a minimization of mean square error.
 16. Thesystem of claim 15, wherein the minimization is based on varying thepower of a disturber and a power of a white noise.
 17. A multicarriercommunications line characterization system comprising: an AM disturberestimation module that determines the presence of one or more AMdisturbers based on an array containing channel noise, a seconddifference of the array and a comparison of one or more carrierfrequencies to a threshold.
 18. The system of claim 17, wherein theestimation module outputs an array containing one or more tone numberscorresponding to a detected AM disturber.
 19. The system of claim 17,wherein the estimation module outputs an array containing a power levelof a detected AM disturber.
 20. The system of claim 17, wherein theestimation module outputs a number representing the number of detectedAM disturbers.